In recent years, research into the automation of traffic data collection with GPS technology has shown remarkable feasibility for replacing traditional resources of traffic data. Paper diaries and phone interviews are two such resources that are heavily depended upon by the traffic and travel research industry. Recent studies compared vehicle-based GPS data to manually recorded data in Travel Diaries to evaluate the efficiency of automated purpose derivation systems. Among its other functions, one of the most consequential uses of travel diaries has been the reporting of an individual's purpose for travel.
Several studies conducted in the past explored implementing GPS data with manual and electronic travel diary submissions. Some of these studies were the first to use passively recorded GPS data. (See Wolf, J., R. Guensler and W. Bachman (2001) “Elimination of the Travel Diary: An Experiment to Derive Trip Purpose from GPS Travel Data,” Transportation Research Record 1768, p. 125-134, Aug. 3, 2006, which is incorporated herein by reference). The study conducted by Wolf et al. utilized a GIS database and GPS data collected by thirteen individuals carrying GPS enabled PDAs. (See also, Wolf, J. (2004) “APPLICATIONS OF NEW TECHNOLOGIES IN TRAVEL SURVEYS,” Submitted to the International Conference on Transport Survey Quality and Innovation, Costa Rica, August 2004, which is incorporated herein by reference). To derive trip purpose, researchers used a point-in-polygon analysis to retrieve a land use code. A set of purposes of varying detail were associated with individual land use codes based on a 1990 Atlanta household travel survey. (See also Atlanta Regional Commission. 1990 Household Travel Study: Final Report. The Atlanta Regional Commission, December, 1993, which is incorporated herein by reference). The land use code was used to derive trip purpose by using a code-purpose association. Wolf et al. faced several obstacles during the land use code categorization step because GIS database used relied on center points. This flaw in the database necessitated manually defining business boundaries in the GIS database based on photographic references. Although the Wolf study was conducted while Atlanta's GIS inventory was still premature, land uses were successfully determined for 145 out of 156 trips.
Another study was conducted by Griffin et al. which concluded that the reliance on geocoded maps to identify locations based on GPS data is impractical because a large percentage of the United States remains to be geocoded. (Griffin, T., Y. Huang and R. Halverson (2006), “Computerized Trip Classification of GPS Data,” International Conference on Cybernetics and Information Technologies, Systems and Applications, Orlando, Fla., which is incorporated herein by reference). Griffin et al. utilized a clustering method known as Dbscan to determine points of interests (POI). POI are simply a cluster of points that were accumulated from an individual frequenting a particular location. These POI were classified by trip purpose based on a decision tree and a learning method known as C4.5. Trip purposes were derived by comparing POI and their established trip purpose to coordinate data transmitted by a GPS enabled PDA. The derivation process, however, was not totally automated. Each POI's purpose had to be manually classified before they could be compared to a GPS coordinate position. The derivation process was no less dependant on human memory than a travel diary as a result of the involvement of human input. The trip classification framework produced correct results between 70% and 97% for all data values despite questionable automation authenticity related to the trip purpose derivation process.